会议专题

Improved Performance of Motor Drive Using RBFNNBased Hybrid Reactive Power MRAS Speed Estimator

Model Reference Adaptive System (MRAS) represents one of the most attractive and popular solutions for sensorless control of induction motor drives. However, the performance of this scheme deteriorates at low speed. A new method is described which considerably improves the performance of MRAS-based sensorless drives in low speed regions of operation. It is applied to a vector-controlled induction motor drive. This new technique uses Radius Basis Function Neural Network (RBFNN) to entirely replace the conventional Proptional- intergral (PI) adaptation mechanism of classical hybrid reactive power MRAS speed estimator. The simulation results show great improvement in the speed estimation performance at low speed.

induction motor hybrid reactive power MRAS Speed estimator Radius Basis Function Neural Network

Jinfeng Xiao Biwen Li Xueyu Gong Yifa Sheng Jun Chai

College of Electrical Engineering University of South China Hengyang,HuNan Province,China College of Mechanical Engineering University of South China Hengyang,HuNan Province,China

国际会议

2010 IEEE信息与自动化国际会议(ICIA 2010)

哈尔滨

英文

1-6

2010-06-20(万方平台首次上网日期,不代表论文的发表时间)